Unpacking your lunch : a qualitative study of young students' ideas about food and nutrition
Bibliographic record
Abstract
This research seeks to understand young students’ ideas about food and nutrition through an environmental education program, the Intergenerational Landed Learning on the Farm Project (ILLP). As a research assistant with this project, I conducted an exploratory study through qualitative lenses by conducting focus groups interviews with 9-10 year old students from a Vancouver elementary school. The interviews were with a class that participated in the ILLP during the school year 2006-2007 and with another class in the same school that did not participate in ILLP. I wondered what the students understand about nutrition and if the students’ experience growing food impacted their understanding about food and nutrition. Environmental and nutrition education are interconnected and share a common responsibility that is crucial for both the environment and the individual. There is no environmental health without individual health, and vice versa; however, this connection has been poorly explored. Nutrition education traditionally has been closer to the prescriptive approach. Research conducted in nutrition education has been related predominately with children’s dietary intake of fruits and vegetables; this type of research has been conducted without including children’s voices. There is a lack of qualitative research exploring children’s understandings about food and nutrition within the context of informal settings. The increase in school gardening projects makes research conducted and reported in this area imperative. My findings suggest that students in both classes (ILLP and non ILLP) held similar views about healthy food. Furthermore, the students from this study share similar ideas about nutrition and food than the ones reported in the literature. Also, my findings suggest that it is important to engage students in more participatory conversations and activities about food and nutrition at the ILLP. Experience alone is not enough to impact their understandings, since children are exposed to a very well-designed marketing industry. The study does provide evidence that the ILLP is meaningful experience that students associate with learning. Through a holistic project like the ILLP that bridges environmental education and nutrition education, I think a different approach to nutrition education is possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".